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An Efficient Artificial Bee Colony Algorithm With an Improved Linkage Identification Method.

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    This study enhances the artificial colony (ABC) algorithm to overcome the curse of dimensionality. New strategies improve convergence speed and search thoroughness for complex optimization problems.

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    Area of Science:

    • Artificial Intelligence
    • Optimization Algorithms
    • Computational Intelligence

    Background:

    • The artificial colony (ABC) algorithm exhibits strong exploration but struggles with high-dimensional and nonseparable functions, leading to slow convergence.
    • Existing ABC variants face limitations due to the curse of dimensionality, hindering performance on complex optimization tasks.

    Purpose of the Study:

    • To improve the performance of the artificial colony (ABC) algorithm, particularly on nonseparable functions and in high-dimensional spaces.
    • To address the convergence speed limitations of ABC by integrating enhanced updating mechanisms and linkage identification.

    Main Methods:

    • An improved linkage identification strategy was embedded into ABC to differentiate between separable and nonseparable functions.
    • Novel strategies were introduced: population-based historical experience for exploration-exploitation balance, optimized scout bee initialization to reduce function evaluations, and multi-dimensional updates for poor performers.
    • The enhanced ABC algorithm was tested on benchmark functions (basic, CEC2010, CEC2013, CEC2014) and real-world problems.

    Main Results:

    • The enhanced ABC algorithm demonstrated improved convergence speed and exploration capabilities on challenging benchmark functions.
    • The proposed strategies effectively addressed the curse of dimensionality, outperforming standard ABC on nonseparable and high-dimensional problems.
    • The multi-dimensional update strategy significantly aided convergence on nonseparable functions.

    Conclusions:

    • The integrated linkage identification and novel updating strategies significantly enhance the artificial colony (ABC) algorithm's performance.
    • The proposed enhancements offer a robust solution for optimizing complex, high-dimensional, and nonseparable functions.
    • These novel concepts are applicable to other ABC variants and optimization algorithms.